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Using service function chains, Internet Service Providers can customize the use of service functions that process the network flows belonging to their customers. Each network flow is injected into a service chain according to the flow features. Since most of the malicious applications try not to get the proper analysis by imitating some valid and famous applications, classification based on simple flow features may waste processing power by using inappropriate service chains for evasive flows. In this paper, we have explored an application-aware classification approach using machine learning methods. Using CatBoost as a machine learning method, a model is created and used for traffic classification. We have provided some statistical reports on how this approach is compared with simple flow feature-based approaches in malicious environments and how feature selection can impact classification correctness. Choosing the most suitable number of features at the right time can beat traditional approaches in classification quality and provide better results in the service function chaining environment.

Summary Virtual networks may be used in a cloud data center to provide personalized networking services for the applications running in the cloud. As the virtual networks come to the data center and leave it, the data center network load may become unbalanced where some parts of the data center has accommodated many virtual networks while a few virtual networks are mapped to other parts of the data center. This situation may lead to packet loss and service level agreement violations in an oversubscribed data center. This unbalanced state of the load in data center can be resolved by migration of virtual networks from overloaded parts of the data center to places where the load is in a lower level. This paper presents implementation details of the prototype of a system that provides virtual networking service in a cloud data center and focuses primarily on virtual network migration as a means for controlling the state of load in data center. Experimental results show that the system has an acceptable performance in reducing the packet loss ratio and keeping the load in a balanced state.

Using virtual networks is a good solution to provide customized networking services in the cloud and to isolate the performance of applications running in the cloud. Cloud providers usually oversubscribe their data centers to reduce the costs imposed to customers and naturally, oversubscription can lead to network overload. If the situation is not managed carefully, it can hurt the customers' experience with the cloud and can also violate the service level agreement between the provider and the customers. This work presents the prototype of a centralized system which is implemented over a software defined data center network and provides a virtual network service that is sensitive to the load in the data center and tries to balance the load by reconfiguring the mapping of the virtual links. So, in an oversubscribed data center, the effects of any virtual network's traffic on other virtual networks' traffic would be reduced. The results of the experiments show that the system can reduce the packet loss in the data center and can increase the aggregate bandwidth that each virtual network receives over its whole life in the data center.